Automatic Segmentation of Dental Cone Beam Computed Tomography Image Based on Level Set Method Using Morphology Operators and Polynomial Fitting

Abstract

Abstract Automatic Segmentation of dental cone beam computed tomography (CBCT) images is challenging due to the intensity of the teeth that have low level intensity. In this paper we proposes a new method for automatic teeth segmentation in slices of CBCT images based on level let method using morphology operators and polynomial fitting. Morphology operators are used to construct the Region of Interest (ROI) area of dental objects in the image slice. ROI is used to focus the analysis process on areas of dental objects which generally have a polynomial pattern distribution. Polynomial fitting is obtained to estimation arc of teeth structure in CBCT images. Level Set is implemented to evolve the ROI to obtain the contours of dental objects. Comparison between proposed method result and the ground truth images shows that the method gives best average accuracy, sensitivity, and specificity value of 99.02%, 95.32%, 99.09%, respectively. This value that the proposed method is promising for accurate segmentation of the entire tooth form on CBCT images.